Identification of Residential Property Sub-Markets using Evolutionary and Neural Computing Techniques

Lewis, Owen M.; Ware, Andrew; Jenkins, David H.

doi:10.1007/s005210170003

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…He found that the market was segmented along structural lines Peng (1999) Compared statistically derived submarkets to geographically based submarkets defined a priori according to territorial local authority submarkets. Method was to use Principal Components Analysis to find the most important orthogonal factors, then use cluster analysis on the uncorrelated principal components to construct six submarkets in Auckland Lewis et al (2001) Used Kohonen Self-Organising Maps (SOMs) and genetic algorithms to create submarket strata based on either property characteristics, or on geodemographic indicators such as socio-economic groupings as defined in the 1991 UK census data Jenkins et al (1998) Used Kohonen self organizing maps (SOMs) to accomplish a segmentation of multidimensional data into submarkets. The SOM is a variant of an artificial neural network or seven different market areas, the variation within each subsegment will still not be accounted for without further granularity in the geographic specification of the model.…”

Section: Mass Appraisal Perspectivementioning

confidence: 99%

Specifying the effect of location in multivariate valuation models for residential properties

McCluskey

Borst²

2007

Property Management

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Purpose -The purpose of this research is to explore from a mass appraisal perspective how the effects of location are reflected within valuation models. The paper sets out to detail the various techniques and the efficacy of their application. Design/methodology/approach -The approach adopted is analytical and based upon the development of locational attributes. An extensive literature base is synthesized with methods being evaluated in their application to mass appraisal. Findings -This research has identified that the three main groups interested in residential property valuation, namely, academia, industry and commerce have to a certain extent been unfamiliar with the research developments occurring in the other groups. The impact of this is important, given the need for integration and collaboration in terms of future model development.Research limitations/implications -The research underpinning this paper will provide a solid basis for further research into this area. The importance of measuring the effect that location has on value is of major significance in the determination of objective estimates of property value. Practical implications -Those within the assessment community could be described as pragmatists working in a situation that requires feasible and suitable solutions to the problem of measuring location value. It is our contention that the third generation techniques of spatially varying parameter models and spatial autocorrelation models will require greater industry verification before their use becomes more widely accepted. Originality/value -This paper provides a detailed analysis of methodologies used to reflect the value of location over the last 50 years. The debate is taken forward by describing what will be the contribution to the development of the next generation of location-specific modeling techniques.

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Section: Mass Appraisal Perspectivementioning

confidence: 99%

Specifying the effect of location in multivariate valuation models for residential properties

McCluskey

Borst²

2007

Property Management

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“…The first usually considers structural property attributes. A specific example of aspatial market segmentation is the application of a Kohonen self organizing map, which is an artificial neuron network, for obtaining the relative positions of nodes in a low‐dimensional attribute space (Jenkins et al , 1998; Lewis et al , 2001; Kauko, 2003). In studies with spatial market segmentation, cluster analysis is applied quite often (Des Rosier, 1991; Case et al , 2004).…”

Section: Literature Reviewmentioning

confidence: 99%

Hedonic price model: defining neighbourhoods with Thiessen polygons

Kryvobokov

2013

Int J of Hous Markts and Analy

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PurposeThe purpose of the paper is to verify whether the version of neighbourhoods created from the lowest geographical level improve a predictive accuracy of hedonic model in comparison with those based on upper geographical levels.Design/methodology/approachThe paper proposes a method for defining neighbourhoods using Thiessen polygons. The clustering technique is based on fuzzy equality. Clustering is started at different geographical levels: municipalities, traffic analysis zones, and apartment blocks' Thiessen polygons. Delineated neighbourhoods are incorporated into hedonic model of apartment prices, the applied methodologies are ordinary least squares and spatial error.FindingsWith ordinary least squares regression, the slight superiority of Thiessen polygons is found in both in‐sample analysis and ex‐sample prediction. With spatial error technique, the clusters of Thiessen polygons do not always provide the best outcome, and their superiority is contested by the highest geographical level of municipalities.Research limitations/implicationsThis paper is the first attempt to apply the proposed method, which not always demonstrates clear superiority. In future study, the method of neighbourhood delineation could be used in combination with market segmentation.Practical implicationsThe proposal to use Thiessen polygons as a transition from points to continuous space can outline a base for the use of different clustering techniques, which are applicable to delineate neighbourhoods in housing market studies, in particular for the assessment purpose. The fuzzy equality clustering algorithm itself can be applied to polygonal data.Originality/valueThe originality of the proposed method is that it defines neighbourhoods starting from individual observations applying fuzzy equality. Its advantages are an increased independence from existing boundaries, self‐determination of a number of clusters, and total coverage of an area.

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“…Kuo et al, (2002aKuo et al, ( ,b, 2006) compared three clustering methods and proposed that SOM performs better clustering than the other conventional methods. Lewis et al, (2001) successfully implemented SOM based neural network to Identify the residential property submarkets. A data mining associatiation ruile based on SOM has been developed and applied to a sample of sales records from database for market fragmentation (Changchien and Lu, 2001).…”

Section: Classification Framework -Ann Dimensionmentioning

confidence: 99%

Application of neural network in market segmentation: A review on recent trends

Chattopadhyay¹,

Dan²,

Majumdar³

et al. 2012

MSL

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Despite the significance of Artificial Neural Network (ANN) algorithm to market segmentation, there is a need of a comprehensive literature review and a classification system for it towards identification of future trend of market segmentation research. The present work is the first identifiable academic literature review of the application of neural network based techniques to segmentation. Our study has provided an academic database of literature between the periods of 2000-2010 and proposed a classification scheme for the articles. One thousands (1000) articles have been identified, and around 100 relevant selected articles have been subsequently reviewed and classified based on the major focus of each paper. Findings of this study indicated that the research area of ANN based applications are receiving most research attention and self organizing map based applications are second in position to be used in segmentation. The commonly used models for market segmentation are data mining, intelligent system etc. Our analysis furnishes a roadmap to guide future research and aid knowledge accretion and establishment pertaining to the application of ANN based techniques in market segmentation. Thus the present work will significantly contribute to both the industry and academic research in business and marketing as a sustainable valuable knowledge source of market segmentation with the future trend of ANN application in segmentation.

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Identification of Residential Property Sub-Markets using Evolutionary and Neural Computing Techniques

Cited by 5 publications

References 6 publications

Specifying the effect of location in multivariate valuation models for residential properties

Specifying the effect of location in multivariate valuation models for residential properties

Hedonic price model: defining neighbourhoods with Thiessen polygons

Application of neural network in market segmentation: A review on recent trends

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